This project represents a unique collaborative opportunity for the modeling and analysis of T cell responses. A quantitative understanding of how CDS and IL-2R signal inputs combine to set the binary growth/death switch is critical for optimizing immunotherapeutic strategies in cancer, viral infection, and autoimmune diseases. We propose to construct predictive dynamic models of transitions between the discrete states of resting, activation, anergy, and apoptosis in cultured primary human T cells. The systems perspective framing these studies is cue/signal/response. The extracellular cues, intracellular signals, and overall responses of human T cells to CDS and IL-2R stimulation will be parsed by the application of state-of-the-art synthetic and analytical methodologies from both the experimental and computational realms. The cues will consist of anti-CDS antibody mimics and high affinity IL-2 mutants engineered by directed evolution to provide quantitatively controlled system inputs. Signals will be tracked at several levels of detail: at the single-cell level by multidimensional flow cytometric measurements of the key signaling molecules; at the population level by Western blot and kinase assay; and at the phosphoproteome level by mass spectrometry. Bayesian inference will be applied to construct influence networks from phosphoproteome data obtained with multivariate flow cytometry and mass spectrometry, partial least squares analysis will be utilized for state identification, and kinetic modeling of signaling pathways will be used to describe and predict the dynamic relationships between cues and signals. This collaborative project will integrate expertise in protein engineering (KDW), theoretical and systems biology (DAL), phosphoproteome mass spectrometry (FW), and cellular immunology and flow cytometry (GPN). These studies will provide insights into T cell regulation as well as develop CDS and IL-2 stimulatory molecules of potential therapeutic value.